Blood vessel diameter in glaucoma

Glaucoma is a leading cause of visual disability in the UK and major referral reason between high street optometry and hospital based ophthalmology. The standard optometric tests used to determine necessity of referral are currently leading to a high false positive burden on glaucoma clinics. The disease of glaucoma is considered to be multifactorial in the reasons for its onset and progression. An increasing body of research proposes a vascular dysregulation hypothesis, and retinal artery diameter reduction, as a recognisable risk factor for both the onset and progression of glaucoma. The Heidelberg Retina Tomograph (HRT) is a commercially available laser scanning ophthalmoscope designed principally for the detection of glaucoma by evaluation of the optic disc neuroretinal rim. An additional ability of the HRT is to measure, via an interactive window, the blood vessels of the scanned image without the need for export of the image or magnification to view them in detail. This thesis contributes to the field of early glaucoma detection by measurement of artery diameter via the interactive window on the HRT machine. The volunteers were divided into three groups normal, glaucoma and ocular hypertensive (OHT) and followed over a period of one year to determine if vessel diameter changed in relation to visual field or neuroretinal rim parameters. The main results in this thesis show that artery diameter does change with glaucoma onset and that the HRT machine is a valid instrument for collection of this data

Generalizable automated pixel-level structural segmentation of medical and biological data

Over the years, the rapid expansion in imaging techniques and equipments has driven the demand for more automation in handling large medical and biological data sets. A wealth of approaches have been suggested as optimal solutions for their respective imaging types. These solutions span various image resolutions, modalities and contrast (staining) mechanisms. Few approaches generalise well across multiple image types, contrasts or resolution. This thesis proposes an automated pixel-level framework that addresses 2D, 2D+t and 3D structural segmentation in a more generalizable manner, yet has enough adaptability to address a number of specific image modalities, spanning retinal funduscopy, sequential fluorescein angiography and two-photon microscopy. The pixel-level segmentation scheme involves: i ) constructing a phase-invariant orientation field of the local spatial neighbourhood; ii ) combining local feature maps with intensity-based measures in a structural patch context; iii ) using a complex supervised learning process to interpret the combination of all the elements in the patch in order to reach a classification decision. This has the advantage of transferability from retinal blood vessels in 2D to neural structures in 3D. To process the temporal components in non-standard 2D+t retinal angiography sequences, we first introduce a co-registration procedure: at the pairwise level, we combine projective RANSAC with a quadratic homography transformation to map the coordinate systems between any two frames. At the joint level, we construct a hierarchical approach in order for each individual frame to be registered to the global reference intra- and inter- sequence(s). We then take a non-training approach that searches in both the spatial neighbourhood of each pixel and the filter output across varying scales to locate and link microvascular centrelines to (sub-) pixel accuracy. In essence, this \link while extract" piece-wise segmentation approach combines the local phase-invariant orientation field information with additional local phase estimates to obtain a soft classification of the centreline (sub-) pixel locations. Unlike retinal segmentation problems where vasculature is the main focus, 3D neural segmentation requires additional exibility, allowing a variety of structures of anatomical importance yet with different geometric properties to be differentiated both from the background and against other structures. Notably, cellular structures, such as Purkinje cells, neural dendrites and interneurons, all display certain elongation along their medial axes, yet each class has a characteristic shape captured by an orientation field that distinguishes it from other structures. To take this into consideration, we introduce a 5D orientation mapping to capture these orientation properties. This mapping is incorporated into the local feature map description prior to a learning machine. Extensive performance evaluations and validation of each of the techniques presented in this thesis is carried out. For retinal fundus images, we compute Receiver Operating Characteristic (ROC) curves on existing public databases (DRIVE & STARE) to assess and compare our algorithms with other benchmark methods. For 2D+t retinal angiography sequences, we compute the error metrics ("Centreline Error") of our scheme with other benchmark methods. For microscopic cortical data stacks, we present segmentation results on both surrogate data with known ground-truth and experimental rat cerebellar cortex two-photon microscopic tissue stacks.Open Acces

Extraction of arterial and venous trees from disconnected vessel segments in fundus images

The accurate automated extraction of arterial and venous (AV) trees in fundus images subserves investigation into the correlation of global features of the retinal vasculature with retinal abnormalities. The accurate extraction of AV trees also provides the opportunity to analyse the physiology and hemodynamic of blood flow in retinal vessel trees. A number of common diseases, including Diabetic Retinopathy, Cardiovascular and Cerebrovascular diseases, directly affect the morphology of the retinal vasculature. Early detection of these pathologies may prevent vision loss and reduce the risk of other life-threatening diseases. Automated extraction of AV trees requires complete segmentation and accurate classification of retinal vessels. Unfortunately, the available segmentation techniques are susceptible to a number of complications including vessel contrast, fuzzy edges, variable image quality, media opacities, and vessel overlaps. Due to these sources of errors, the available segmentation techniques produce partially segmented vascular networks. Thus, extracting AV trees by accurately connecting and classifying the disconnected segments is extremely complex. This thesis provides a novel graph-based technique for accurate extraction of AV trees from a network of disconnected and unclassified vessel segments in fundus viii images. The proposed technique performs three major tasks: junction identification, local configuration, and global configuration. A probabilistic approach is adopted that rigorously identifies junctions by examining the mutual associations of segment ends. These associations are determined by dynamically specifying regions at both ends of all segments. A supervised Naïve Bayes inference model is developed that estimates the probability of each possible configuration at a junction. The system enumerates all possible configurations and estimates posterior probability of each configuration. The likelihood function estimates the conditional probability of the configuration using the statistical parameters of distribution of colour and geometrical features of joints. The parameters of feature distributions and priors of configuration are obtained through supervised learning phases. A second Naïve Bayes classifier estimates class probabilities of each vessel segment utilizing colour and spatial properties of segments. The global configuration works by translating the segment network into an STgraph (a specialized form of dependency graph) representing the segments and their possible connective associations. The unary and pairwise potentials for ST-graph are estimated using the class and configuration probabilities obtained earlier. This translates the classification and configuration problems into a general binary labelling graph problem. The ST-graph is interpreted as a flow network for energy minimization a minimum ST-graph cut is obtained using the Ford-Fulkerson algorithm, from which the estimated AV trees are extracted. The performance is evaluated by implementing the system on test images of DRIVE dataset and comparing the obtained results with the ground truth data. The ground truth data is obtained by establishing a new dataset for DRIVE images with manually classified vessels. The system outperformed benchmark methods and produced excellent results

Human retinal oximetry using hyperspectral imaging

The aim of the work reported in this thesis was to investigate the possibility of measuring human retinal oxygen saturation using hyperspectral imaging. A direct non-invasive quantitative mapping of retinal oxygen saturation is enabled by hyperspectral imaging whereby the absorption spectra of oxygenated and deoxygenated haemoglobin are recorded and analysed. Implementation of spectral retinal imaging thus requires ophthalmic instrumentation capable of efficiently recording the requisite spectral data cube. For this purpose, a spectral retinal imager was developed for the first time by integrating a liquid crystal tuneable filter into the illumination system of a conventional fundus camera to enable the recording of narrow-band spectral images in time sequence from 400nm to 700nm. Postprocessing algorithms were developed to enable accurate exploitation of spectral retinal images and overcome the confounding problems associated with this technique due to the erratic eye motion and illumination variation. Several algorithms were developed to provide semi-quantitative and quantitative oxygen saturation measurements. Accurate quantitative measurements necessitated an optical model of light propagation into the retina that takes into account the absorption and scattering of light by red blood cells. To validate the oxygen saturation measurements and algorithms, a model eye was constructed and measurements were compared with gold-standard measurements obtained by a Co-Oximeter. The accuracy of the oxygen saturation measurements was (3.31%± 2.19) for oxygenated blood samples. Clinical trials from healthy and diseased subjects were analysed and oxygen saturation measurements were compared to establish a merit of certain retinal diseases. Oxygen saturation measurements were in agreement with clinician expectations in both veins (48%±9) and arteries (96%±5). We also present in this thesis the development of novel clinical instrument based on IRIS to perform retinal oximetry.Al-baath University, Syri

Vessel identification in diabetic retinopathy

Diabetic retinopathy is the single largest cause of sight loss and blindness in 18 to 65 year olds. Screening programs for the estimated one to six per- cent of the diabetic population have been demonstrated to be cost and sight saving, howeverthere are insufficient screening resources. Automatic screen-ing systems may help solve this resource short fall. This thesis reports on research into an aspect of automatic grading of diabetic retinopathy; namely the identification of the retinal blood vessels in fundus photographs. It de-velops two vessels segmentation strategies and assess their accuracies. A literature review of retinal vascular segmentation found few results, and indicated a need for further development. The two methods for vessel segmentation were investigated in this thesis are based on mathematical morphology and neural networks. Both methodologies are verified on independently labeled data from two institutions and results are presented that characterisethe trade off betweenthe ability to identify vesseland non-vessels data. These results are based on thirty five images with their retinal vessels labeled. Of these images over half had significant pathology and or image acquisition artifacts. The morphological segmentation used ten images from one dataset for development. The remaining images of this dataset and the entire set of 20 images from the seconddataset were then used to prospectively verify generaliastion. For the neural approach, the imageswere pooled and 26 randomly chosenimageswere usedin training whilst 9 were reserved for prospective validation. Assuming equal importance, or cost, for vessel and non-vessel classifications, the following results were obtained; using mathematical morphology 84% correct classification of vascular and non-vascular pixels was obtained in the first dataset. This increased to 89% correct for the second dataset. Using the pooled data the neural approach achieved 88% correct identification accuracy. The spread of accuracies observed varied. It was highest in the small initial dataset with 16 and 10 percent standard deviation in vascular and non-vascular cases respectively. The lowest variability was observed in the neural classification, with a standard deviation of 5% for both accuracies. The less tangible outcomes of the research raises the issueof the selection and subsequent distribution of the patterns for neural network training. Unfortunately this indication would require further labeling of precisely those cases that were felt to be the most difficult. I.e. the small vessels and border conditions between pathology and the retina. The more concrete, evidence based conclusions,characterise both the neural and the morphological methods over a range of operating points. Many of these operating points are comparable to the few results presented in the literature. The advantage of the author's approach lies in the neural method's consistent as well as accurate vascular classification

The Retinal Microvasculature in Secondary Progressive Multiple Sclerosis

In light of new data regarding pathology of multiple sclerosis (MS), more research is needed into the vascular aspects of the disease. Demyelination caused by inflammation is historically thought of as the main cause of disability in the disease. Recent studies, however, have suggested that MS is in fact a spectrum of overlapping phenotypes consisting of inflammation, oxidative damage and hypoperfusion. The microvasculature plays an important role in all of these pathogenic processes and its dysfunction may therefore be of crucial importance to the development and progression of the disease. This thesis focuses on investigating the microvasculature of the retina as a surrogate for the brain by assessing the vascular structure, blood flow dynamics and oxygen transfer of the retinal blood vessels in secondary progressive multiple sclerosis (SPMS). Studying the retinal microvasculature using a multimodal imaging approach has allowed us to develop a more detailed understanding of blood flow in MS and to identify new imaging markers for trials into neuroprotective drugs in MS. The work done in this thesis demonstrated; i) a higher rate of retinal microvascular abnormalities in MS which progresses with disease severity, ii) evidence of retinal vascular remodelling in SPMS and iii) changes in blood velocity and flow in the retina in SPMS. These observations pave the way for future investigations into the mechanisms of vascular alterations and vascular dysfunction in MS, and provide a set of imaging markers to further explore other cerebrovascular diseases through the retina